#importing all the libraies

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error
from sklearn import metrics
from sklearn.metrics import mean_squared_error,mean_absolute_error
import plotly.express as px
import plotly.graph_objects as go

# taking the data set input 
data = pd.read_csv("C:/Users/KIIT/Desktop/Group Project/diamonds.csv")
data.head(5)

	Unnamed: 0	carat	cut	color	clarity	depth	table	price	x	y	z
0	1	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	2	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	3	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	4	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	5	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

# Shape 
data.shape

(53940, 11)

Data Preprocessing

Steps involved in Data Preprocessing

Data Cleaning

Identifying and removing the outliers

Encoding categorical variables

# Removing the first column because it is the same as index
data.drop(["Unnamed: 0"], axis = 1)    #Axis 1 for selecting columns 
data.describe()

	Unnamed: 0	carat	depth	table	price	x	y	z
count	53940.000000	53940.000000	53940.000000	53940.000000	53940.000000	53940.000000	53940.000000	53940.000000
mean	26970.500000	0.797940	61.749405	57.457184	3932.799722	5.731157	5.734526	3.538734
std	15571.281097	0.474011	1.432621	2.234491	3989.439738	1.121761	1.142135	0.705699
min	1.000000	0.200000	43.000000	43.000000	326.000000	0.000000	0.000000	0.000000
25%	13485.750000	0.400000	61.000000	56.000000	950.000000	4.710000	4.720000	2.910000
50%	26970.500000	0.700000	61.800000	57.000000	2401.000000	5.700000	5.710000	3.530000
75%	40455.250000	1.040000	62.500000	59.000000	5324.250000	6.540000	6.540000	4.040000
max	53940.000000	5.010000	79.000000	95.000000	18823.000000	10.740000	58.900000	31.800000

Now removing the "x","y","z", values which are zero because they are faulty values

data = data.drop(data[data["x"]==0].index)
data = data.drop(data[data["y"]==0].index)
data = data.drop(data[data["z"]==0].index)
data.shape       #data shape after remobing the "x","y","z" values having 0

(53920, 11)

We lost 20 data points by deleting the dimensionless(2D or 1D) diamonds.

Exploratory Data analysis

figure = px.scatter(data_frame = data, x = "carat", y= "price",size = "depth",color = "cut",trendline = "ols")
figure.show()

The above figure concludes two features of diamonds:

1. Premium cut diamond are relatively larger than other diamonds.

2. There's linear relationship between the size of all types of diamonds       and their prices. 

Now let's have a look at the price of all the types of all the types of diamonds based on their colour:

fig  = px.box(data, x = "cut", y = "price",color = "color")
fig.show()

Now let's have a look at the price of all the types of diamonds based on their clarity:

fig= px.box(data, x = "cut",y = "price",color = "color")
fig.show()

Now let's have a look at the price of all the types of diamonds based on their clarity :

fig = px.box(data, x = "cut", y = "price", color ="clarity")
fig.show()

Data Preprocessing 2

data['cut'] = data['cut'].map({'Ideal':5,'Premium': 4, 'Very Good' :3,'Good':2,'Fair':1})
data['color'] = data['color'].map({'D':7,'E':6,'F':5,'G':4,'H':3,'I':2,'J':1})
data['clarity']=data['clarity'].map({'IF':8,'VVS1':7,'VVS2':6,'VS1':5,'VS2':4,'SI1':3,'ST2':2,'IF':1})

Correlation

Now let's have a look at the correlation between diamonds prices and other features in the dataset:

data.corr()

	Unnamed: 0	carat	cut	color	clarity	depth	table	price	x	y	z
Unnamed: 0	1.000000	-0.378173	0.096584	0.095062	0.085568	-0.035058	-0.100872	-0.307092	-0.406331	-0.396480	-0.401758
carat	-0.378173	1.000000	-0.134953	-0.291360	-0.157390	0.028259	0.181646	0.921592	0.977779	0.953991	0.961048
cut	0.096584	-0.134953	1.000000	0.020517	0.074140	-0.218073	-0.433306	-0.053491	-0.126232	-0.122181	-0.150647
color	0.095062	-0.291360	0.020517	1.000000	0.001894	-0.047373	-0.026481	-0.172431	-0.270671	-0.263915	-0.270011
clarity	0.085568	-0.157390	0.074140	0.001894	1.000000	-0.033082	-0.060149	-0.063147	-0.167572	-0.164300	-0.165640
depth	-0.035058	0.028259	-0.218073	-0.047373	-0.033082	1.000000	-0.295733	-0.010729	-0.025017	-0.029069	0.095023
table	-0.100872	0.181646	-0.433306	-0.026481	-0.060149	-0.295733	1.000000	0.127245	0.196097	0.184493	0.152483
price	-0.307092	0.921592	-0.053491	-0.172431	-0.063147	-0.010729	0.127245	1.000000	0.887231	0.867864	0.868206
x	-0.406331	0.977779	-0.126232	-0.270671	-0.167572	-0.025017	0.196097	0.887231	1.000000	0.974918	0.975435
y	-0.396480	0.953991	-0.122181	-0.263915	-0.164300	-0.029069	0.184493	0.867864	0.974918	1.000000	0.956744
z	-0.401758	0.961048	-0.150647	-0.270011	-0.165640	0.095023	0.152483	0.868206	0.975435	0.956744	1.000000

Plotting the correlation heatmap

plt.figure(figsize = (10,10))
sns.heatmap(data.corr(),annot = True, cmap = 'coolwarm')
plt.title('Correlation Heatmap')
plt.show()

Plotting the relationship between Price and Carat

sns.lineplot(x = 'carat',y ='price', data = data)
plt.title('Carat vs Price')
plt.show()

From the lineplot it is quite clear that the price of diamonds increase with the increase in the carat of the diamond. However, Diamonds with less carat also have a high price. This is because of the other factors that affect the price of the diamonds.

Train Test Split

x_test,x_train,y_test,y_train = train_test_split(data.drop('price',axis = 1),data['price'])

Model Building

Decision Tree Regressor

dt = DecisionTreeRegressor()
dt

DecisionTreeRegressor()

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Train the model

dt.fit(x_train,y_train)

DecisionTreeRegressor()

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Train accuracy

dt.score(x_train,y_train)

0.8863104872886358

#predicting the test set
dt_pred = dt.predict(x_test)

Model Evaluation

Distribution plot for actual and predict values

ax = sns.distplot(y_test, hist = False, color = 'y', label = 'Actual Value')
sns.distplot(dt_pred,hist = False, color = 'b', label = 'Fitted Values', ax = ax)
plt.title('Actual vs Fitted Values for Price')
plt.xlabel('Price')
plt.ylabel('Proportion of Diamonds')
plt.show()

C:\Users\KIIT\AppData\Local\Temp\ipykernel_18288\1239737684.py:1: UserWarning:



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


C:\Users\KIIT\AppData\Local\Temp\ipykernel_18288\1239737684.py:2: UserWarning:



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

print("Decision Tree Regressor RMS:" , np.sqrt(mean_squared_error(y_test,dt_pred)))
print('Decision Tree Regressor ACCURACY:',dt.score(x_test,y_test))
print('Decision Tree Regressor MAE:', mean_absolute_error(y_test,dt_pred))
      

Decision Tree Regressor RMS: 1370.9863413672801
Decision Tree Regressor ACCURACY: 0.8809295133256525
Decision Tree Regressor MAE: 1060.198550716927